chinese-offensive-lang-detection-with-cnn's Introduction

chinese-offensive-lang-detection-with-cnn

Pytorch implementation of the CNN for Chinese offensive language detection

Setup

Python >3.8 is required.

Create virtual environment

virtualenv coldcnn_env
source coldcnn_env/bin/activate
pip install -r requirements.txt
python -m spacy download zh_core_web_md

Install the `pre-commit` hooks

pre-commit install
pre-commit run --all-files

Preprocess

To remove the stopwords from the text messages, a filter can be applied over the CJK Unified Ideographs. CJK stands for Chinese, Japanese and Korean. Code ranges related to CJK include the follows.

31C0—31EF CJK Strokes
31F0—31FF Katakana Phonetic Extensions
3200—32FF Enclosed CJK Letters and Months
3300—33FF CJK Compatibility
3400—4DBF CJK Unified Ideographs Extension A
4DC0—4DFF Yijing Hexagram Symbols
4E00—9FFF CJK Unified Ideographs

Model architecture

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv1d-1              [-1, 16, 108]          14,416
              ReLU-2              [-1, 16, 108]               0
         MaxPool1d-3               [-1, 16, 53]               0
            Conv1d-4              [-1, 16, 107]          19,216
              ReLU-5              [-1, 16, 107]               0
         MaxPool1d-6               [-1, 16, 52]               0
            Conv1d-7              [-1, 16, 106]          24,016
              ReLU-8              [-1, 16, 106]               0
         MaxPool1d-9               [-1, 16, 51]               0
          Flatten-10                 [-1, 2496]               0
          Dropout-11                 [-1, 2496]               0
           Linear-12                  [-1, 128]         319,616
          Dropout-13                  [-1, 128]               0
           Linear-14                    [-1, 1]             129
          Sigmoid-15                    [-1, 1]               0
================================================================
Total params: 377,393
Trainable params: 377,393
Non-trainable params: 0
----------------------------------------------------------------